Learning a bayesian network model for predicting wildfire behavior

K. Zwirglmaier, P. Papakosta, D. Straub

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

A Bayesian Network (BN) model for predicting wildfire spreading was developed. From the available indicator variables related to weather, topography and land cover, the most informative were selected with the help of automatic structure learning algorithms. A final BN model was then constructed from these indicators using phenomenological reasoning. Automatic structure learning of the complete model was found to have severe limitations due to large number of variables in combination with limited number of observations. The BN model was learned and validated with data from the Mediterranean island of Cyprus. The final BN was compared to a Naïve Bayesian Classifier (NBC), which serves as a benchmark, and it was shown to be applicable for prediction purposes.

Original languageEnglish
Title of host publicationSafety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures - Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013
Pages3115-3121
Number of pages7
StatePublished - 2013
Event11th International Conference on Structural Safety and Reliability, ICOSSAR 2013 - New York, NY, United States
Duration: 16 Jun 201320 Jun 2013

Publication series

NameSafety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures - Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013

Conference

Conference11th International Conference on Structural Safety and Reliability, ICOSSAR 2013
Country/TerritoryUnited States
CityNew York, NY
Period16/06/1320/06/13

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